# dataset settings
from mmcv import LoadImageFromFile, RandomResize, RandomFlip, Resize, TestTimeAug
from mmengine.dataset import InfiniteSampler, DefaultSampler
from mmseg.datasets import ADE20KDataset, LoadAnnotations, RandomCrop, PhotoMetricDistortion, PackSegInputs
from mmseg.evaluation import IoUMetric

dataset_type = ADE20KDataset
data_root = 'data/ade/ADEChallengeData2016'
crop_size = (512, 512)
train_pipeline = [
    dict(type=LoadImageFromFile),
    dict(type=LoadAnnotations, reduce_zero_label=True),
    dict(
        type=RandomResize,
        scale=(2048, 512),
        ratio_range=(0.5, 2.0),
        keep_ratio=True,
        resize_type=Resize,
    ),
    dict(type=RandomCrop, crop_size=crop_size, cat_max_ratio=0.75),
    dict(type=RandomFlip, prob=0.5),
    dict(type=PhotoMetricDistortion),
    dict(type=PackSegInputs)
]
test_pipeline = [
    dict(type=LoadImageFromFile),
    dict(type=Resize, scale=(2048, 512), keep_ratio=True),
    # add loading annotation after ``Resize`` because ground truth
    # does not need to do resize data transform
    dict(type=LoadAnnotations, reduce_zero_label=True),
    dict(type=PackSegInputs)
]
img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
tta_pipeline = [
    dict(type=LoadImageFromFile, backend_args=None),
    dict(
        type=TestTimeAug,
        transforms=[
            [
                dict(type=Resize, scale_factor=r, keep_ratio=True)
                for r in img_ratios
            ],
            [
                dict(type=RandomFlip, prob=0., direction='horizontal'),
                dict(type=RandomFlip, prob=1., direction='horizontal')
            ], [dict(type=LoadAnnotations)], [dict(type='PackSegInputs')]
        ])
]
train_dataloader = dict(
    batch_size=4,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type=InfiniteSampler, shuffle=True),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        data_prefix=dict(
            img_path='images/training',
            seg_map_path='annotations/training'
        ),
        pipeline=train_pipeline)
)
val_dataloader = dict(
    batch_size=1,
    num_workers=4,
    persistent_workers=True,
    sampler=dict(type=DefaultSampler, shuffle=False),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        data_prefix=dict(
            img_path='images/validation',
            seg_map_path='annotations/validation'),
        pipeline=test_pipeline)
)
test_dataloader = val_dataloader

val_evaluator = dict(type=IoUMetric, iou_metrics=['mIoU'])
test_evaluator = val_evaluator
